Financial Networks and Trading in Bond Markets

Financial Networks and Trading in Bond MarketsSchool of Management, University of Texas at Dallas This paper examines the role of financial networks in influencing asset prices and trad

Financial Networks and Trading in Bond Markets

School of Management, University of Texas at Dallas

This paper examines the role of financial networks in influencing asset prices and trading performance.Consistent with theoretical studies on the role of communication networks in information dissemination, we positthat financial institutions with more extensive financial networks (global financial institutions) can more efficientlyacquire and process information pertaining to asset trading in global financial markets than financial institutionswith more limited financial networks (local financial institutions) The information advantage affords the globalfinancial institutions more favorable transaction prices and better trading performance relative to their localcounterparts Using transaction-level Turkish government bond trading data, we find that financial institutions withglobal financial networks exhibit a stronger tendency to trade in the more liquid bonds Further, they consistentlytrade at more favorable prices and enjoy better trading performance than local financial institutions Together, theseresults suggest that global financial institutions have information advantages and benefit from bond trading in anopen emerging market

Key Words: Financial Networks, Information, Bond Markets

1 Introduction

Although it is well established that information moves security prices, how information flowsthrough financial markets and is incorporated in the prices of financial assets is not as well understood.Traditional asset pricing approaches assume that individual agents behave anonymously with newinformation becoming known by all the agents in the market simultaneously, thereby making theinformation common knowledge Information, however, can also gradually spread throughout the market

by word-of-mouth and observational learning Because of differences in institutional structures andtraders’ information processing abilities, it is unlikely that information diffusion will be amorphous.Instead, information is likely to spread more rapidly within trading firms than between trading firms, notonly because of the presence of an intra-firm network but also because of financial incentives provided totraders that are related to firm profitability As a result, traditional approaches disregard the possibilitythat agent behavior (individually and collectively) may be influenced by a communication network

Models of trading dynamics recognize the presence of asymmetric information The distinctionbetween informed and uninformed traders leads to a number of useful insights For instance, informedtraders tend to respond more quickly to news, tend to trade in more liquid markets, and tend to showbetter performance than uninformed traders Yet it is not entirely clear who the informed traders are orhow they become informed In this regard several empirical studies show that individuals who reside andwork in the same location tend to make similar financial decisions, which suggests the presence ofinternal group communication.1 The idea is that traders who are spatially and electronically close areexposed to similar information that is diffused via networks within the same group once the information isreceived by one or more of the traders

Simonov, 2006), as do professional money managers (Coval and Moskowitz, 2001) Investors also tend to follow their

Hong, Kubik, and Stein (2004) develop a model in which stock market participation is influenced by social interaction, and Xia shows (2007) that the influence of information on transaction prices depends on the structure of the network

We address this gap in the literature by comparing the information networks of financialinstitutions that trade bonds in an emerging market We select this type of market because, as Biais andGreen (2005) point out, bond markets often provide little pre-trading transparency, which createsopportunities for informed traders to take advantage of their superior information We classify the sampleinstitutions as those that have offices in the local economy only and those that have offices both in thelocal economy and in major bond trading markets such as New York City and London We define afinancial network to be a set of offices that are linked together by an electronic communication system

Consistent with the implications of theoretical studies on the role of networks in informationdissemination, we posit that a financial institution with a global (more extensive) financial network canmore efficiently acquire and process information related to global financial market movements than aninstitution with only a local (less extensive) network This information advantage is expected to allowglobal financial institutions to trade more nimbly and perform better relative to local financial institutions

We test our hypotheses by empirically investigating day trading in the government bonds ofTurkey, an open emerging market in which financial institutions with different scopes of financialnetworks are permitted to participate with limited government interference We find that financialinstitutions with global financial networks trade at more favorable prices and demonstrate betterperformance in the Turkish government bond market relative to financial institutions with only localnetworks Our empirical findings thus support the conjecture that financial institutions with a moreextensive financial network such as those with a global network exhibit an information advantage andbenefit from utilizing their superior information relative to financial institutions with a less extensivefinancial network such as those with only a local network

2 The Bonds and Bills Market

2.1 The Market

Turkey’s public bond market, the Bonds and Bills Market, is an important investment and tradingvenue for financial institutions Using total market capitalization standardized by GDP as a measure ofimportance, according to World Bank Database on Financial Development and Structure, Turkey ranked

9th out of 30 major world bond markets, with its bond market being 2.3 times as large as its equity market(see Beck et al (2000) for details of this database)

Almost every month, the Turkish Treasury auctions bonds with maturities ranging from onemonth to 10 years After the primary market allocation, these bonds are traded on an automated secondarymarket, the Bonds and Bills Market This market also facilitates repurchase agreements, but thesetransactions are executed separately and excluded from our analysis The institutions that are authorized

to trade on the Bonds and Bills Market are Istanbul Stock Exchange (ISE) member banks and memberbrokerage houses These financial institutions typically trade on their own accounts Sometimes they fillretail buy orders from their inventory, but if their inventory is insufficient they may have to go to market

to meet demand

Each institution employs multiple traders who form an information network They are in constantcontact with each other throughout the trading day, permitting them to be better aware of the local buyand sell order flow For instance, it is not uncommon for traders to inform the participants in their networkthat they have learned that a particular financial institution is a net buyer today or that another financialinstitution is trying to liquidate a sizeable position Some institutions have home offices in multiplemarkets while others have branches; such organizational structures create multi-market trader networksthat facilitate the transmission of information relevant to the local market

Bond market participants are a diverse mix of small and large Turkish financial institutions andlarge international financial institutions These institutions have different arrangements to disseminatinginformation International banks, for instance, have their bond trading floors connected by a “hoot”.Nowadays a “hoot” refers to an electronic communication system, but originally it was a device devoted

to a single trading floor “Hoot” transmissions tend to flow from New York and London to other markets

In contrast, bond traders of Turkish banks (especially large Turkish banks) gather information by makingphone calls to fellow bond traders in overseas financial centers Of course, information is also available toall traders whose firms have access to public information networks such as Reuters, Bloomberg, andsimilar providers Different financial institutions, however, may still have different informationprocessing capabilities, which may lead to differences in interpretation of publicly released informationand in turn trading performance.

2.2 The Trading System

The Bonds and Bills Market is a limit order book market that uses an electronic system to match,administer, and report transactions The market operates in two sessions: from 9:30 a.m to 12:00 noon,and from 1:00 p.m to 5:00 p.m Bonds with same-day and next-day settlement trade until 2:00 p.m.,which is the settlement time for the day; between 2:00 p.m and closing, only bonds with next-daysettlement trade Thus, the number of transactions declines noticeably after 2:00 p.m

Orders are processed and executed according to price and time priority in an automated trading system The ISE uses an order-driven electronic continuous market with no intermediary such as a market

maker and no floor brokers The majority of the orders are routed electronically via member firms to thecentral limit order book through an order processing system that does not require any re-entry by themember firms In very rare cases, member firms call representatives at the exchange to have their ordersentered for them Member firms can execute market orders and limit orders, as well as orders that requirefurther conditions for execution (e.g., Fill-or-kill and Stop-loss) Member firms are not allowed to enterorders when the market is not open; however, they are allowed to withdraw their existing orders It is notunusual to see traders filling out their order screen prior to opening time and submitting multiple orders atthe open

Price information on the 20 best bids and offers is continuously available to member firms Thesystem does not display quantity demanded or offered at each of these prices, but past transactions can beviewed by all members The tick size is 1 Turkish lira (TL) for a 100,000 TL face value bond, with

minimum (maximum) order size set to 100,000 TL (10 million TL); there exists no formal upstairs market

for block trades An incoming market order is executed automatically against the best limit orders in the book Execution within the inside quotes is allowed.

Once a transaction takes place, a confirmation notice is sent to the parties involved in thetransaction The other market participants do not learn the identities of the parties, but they do observethat a transaction took place at a specific price and quantity All information pertaining to price, yield, andvolume of best orders as well as details of the last transaction and a summary of all transactions aredisseminated to data vendors, including Bloomberg, Reuters, and some local firms, immediately aftereach transaction In addition, all trades are reported to the clearing organization, the ISE Settlement andCustody Bank Inc (Takas Bank), at the end of the day to facilitate bookkeeping We do not haveinformation on what percentage of the transactions take place in ISE; however, anecdotal evidencesuggests that ISE consolidates more than 97% of the turnover value of the Bonds and Bills Market’stransactions The remaining portion is captured by OTC markets

The Turkish government typically plays a minimal role in the Bonds and Mills Market.Nevertheless, after the 2001 banking crisis, the Undersecretariat of the Treasury initiated a primary dealersystem that requires some market’s members that participate in the primary market auction to provideliquidity by quoting a bid and an ask (not necessarily the best bid or ask) in the secondary market Thequotes are identified as being given by a primary dealer The rationale for this innovation is that thesemembers would accommodate liquidity needs that may arise during times of crisis, although anecdotalevidence indicates that such action by the primary dealers has yet to occur The number of primary dealermembers (typically between eight and 14) and its composition (foreign or domestic) is determined by theUndersecretariat In 2006, the primary dealer system consisted of 12 primary dealers The most recentlyissued bond is designated as the active (or benchmark) bond

3 Data and Summary Statistics

Our sample consists of 1,716,917 tick-by-tick time-stamped transactions beginning May 1, 2001and ending June 15, 2005 (1,039 trading days) For each transaction, we have detailed information on thetime of order placed and filled, transaction price, and trade size for 177 Turkish lira-denominatedTreasury bills and notes More important, our data set also contains the identities of the traders on bothsides of a transaction from their unique identification code The starting date of the sample is two monthsafter the Turkish financial crisis attributed to liquidity shortages in the banking system that ended inFebruary 2001 Data availability dictates the sample’s ending date

One hundred seventy distinct financial institutions participated in the Bonds and Bills Market

We classify these into local versus global financial institutions A financial institution is classified as

“global” if it has branches or offices in major financial markets outside Turkey; otherwise, it is classified

as “local” Based on information collected from the Istanbul Stock Exchange and data from the TurkishBank Association ( tpht ://www.tbb.org.tr/net/subeler) on overseas branches and offices, we classify 146 aslocal financial institutions and 24 as global financial institutions We use the ISE asset size categories todivide local financial institutions into 116 small and 30 large financial institutions.2

The roster of global financial institutions includes large foreign banks such as Deutsche Bank,Citibank, and JP Morgan Chase as well as large domestic financial institutions such as Yapı Kredi BankasıA.Ş., Vakıflar Bankası A.Ş., and Akbank A.Ş The foreign global financial institutions have home offices

in New York and throughout Europe, with the latter including offices in London, Amsterdam, Paris, andFrankfurt to name but a few Six of the global financial institutions are Turkish and together account formore than 22% of the global financial institutions’ participation in the Turkish market These financialinstitutions have branch and liaison offices not only in New York and Europe but also in Bahrain, Tokyo,and Moscow.3

proxies for size are highly correlated This is not surprising since anecdotal evidence suggests that large financial institutions participate in treasury auctions more frequently and have the ability to obtain more bonds.

banking services (deposits and remittances) We performed our analyses with these banks classified as global financial institutions Our results were not affected.

Using the trader identification code, our global versus local classification, and ISE asset sizecategories, we classify each transaction as being made by a local small, local large, or global financialinstitution Table 1 reports selective summary statistics for our data Panel A shows the average dailytrading volume in U.S dollars (USD) of the local small, local large, and global financial institutionssorted by seller- and buyer-initiated trades and their counterparties.4 Of the USD 640 million of total dailyvolume, trading between local large and global financial institutions is the highest, with average dailytrading volume reaching USD 133.8 million for seller-initiated trades and USD 139 million for buyer-initiated trades Trading among global financial institutions is the second highest, with seller- and buyer-initiated daily trading volume of USD 76.5 million and USD 95.6 million, respectively

In Panel B, we report the average size and number of tick-by-tick transactions for local small,local large, and global financial institutions without reference to the initiator The average volume pertransaction is highest, at USD 0.9 million, between local large and global financial institutions Thetransaction volume for trades among global financial institutions is the second highest at USD 0.65million, while trades between local small and global financial institutions ranks third In terms of thenumber of transactions, trades between local large and global financial institutions account for 36.9% ofall trades, trades among local large financial institutions account for 21.7%, and trades among globalfinancial institutions account for 16.1%

4 Empirical Analysis and Results

Our analysis consists of three distinct but related analyses We first examine whether globalfinancial institutions are more likely to trade more liquid bonds, a practice that allows them to more easilyhide informed strategic trades We then investigate whether global financial institutions consistentlytransact at more favorable prices Finally, we explore the day-trading profitability by different financial

currency at the end of 2004 We incorporate this change in our calculations During the sample period, the average exchange rate was TL 1.46 = USD 1 with a standard deviation of TL 0.11

institutions The evidence supports the notion that financial institutions with global information networksare more informed than those institutions with only local networks

4.1 Strategic Trading

Chowdry and Nanda (1991) show that informed investors tend to trade in more liquid markets,presumably because of their need to hide strategic transactions that convey information Combining thisobservation with Pasquariello and Vega’s (2007) suggestion that the most liquid bonds are the ones thatare most recently issued, we hypothesize that if global financial institutions have an informationaladvantage over local financial institutions, they tend to trade active bonds, i.e., the most recently issuedbonds, relative to the other bonds, which we refer to as passive bonds

In Table 2 we report different financial institutions’ average daily transactions—measured byvolume of trade (in U.S dollars) and number of trades—in active bonds and passive bonds and the dailyratio of trading in active bonds to passive bonds Consistent with Pasquariello and Vega (2007), activebonds have the highest transaction volume/number compared to the rest of the bonds As a robustness

check, we calculate the transaction volume at day t-1 and designate the bond with highest score as the

active bond for the next trading day Our conclusions are not affected by this alternative definition of

“active” bond

Next, we split the sample transactions into cases where global financial institutions are on bothsides of the transaction, a global financial institution is on one side and a local financial institution on theother, and local financial institutions are on both sides The results for trading volume and number oftransactions are similar, although those for trading volume are more compelling The ratio of active topassive bonds is greatest in terms of trading volume (in U.S dollars) when global financial institutions are

on both sides of the transaction and smallest when local financial institutions are on both sides of thetrade For example, the ratio of active to passive bonds in terms of trading volume is 2.33 for globalfinancial institutions and 1.38 for local financial institutions The difference in the ratios of active topassive bonds for global/global and local/local financial institutions is significant (p = 0.000), suggesting

that global financial institutions have a stronger preference for trading active bonds than local financialinstitutions

The results based on the number of transactions are similar While the ratio of active to passivebonds for trades involving only global financial institutions is 1.34, the corresponding ratio for only localfinancial institutions is 1.19 The difference is again significant (p = 0.000) Our empirical evidence onpreferences of global and local financial institutions with respect to active versus passive bonds thussupports the conjecture that global financial institutions have an information advantage over localfinancial institutions This evidence may also be consistent, however, with alternative explanations such

as the liquidity concerns of global financial institutions trading large quantities

4.2 Pricing Advantage

We define a financial institution as better “informed” if it consistently buys (or sells) before otherfinancial institutions before the market price rises (or declines) Similar to Massa and Simonov (2003), wedetermine the degree of informativeness of different financial institutions by examining the delayed price

changes in the same bond in a D-minute interval following each transaction initiated by the financial institution For a given bond k, we estimate the following time-series regression to identify the delayed price changes associated with trades initiated by investor i:

where  Pik D is the delayed price change of bond k in D minutes following the trade, I k is a binary

indicator that takes a value of one for bond k (the bond fixed effect), and T ik is the signed transactionvolume in million Turkish liras (positive for purchases and negative for sales) The notation for the

subscript indicates that investor i initiated trade of bond k θ ik measures the effect on the delayed pricechange ( Pik D ) of a trade in bond k initiated by investor i For  Pik Dto be defined, there must be a

transaction in bond k within D minutes after the trade If there is more than one trade, we use the last

transaction within the interval, and if no transaction takes place, we set  Pik D= 0

We use a 10-minute time interval (D = 10) as the baseline case in our regression to identify the

price impact of informed trades Given the total number of trades and trading days in our sample, onaverage there are about 30 trades per 10-minute interval We also perform our analysis using 20- and 30-minute time intervals, which increases the average number of trades per interval to approximately 60 and

90, respectively This analysis allows us to assess how quickly information dissipates in this market.Excluding the top and bottom deciles of trades from each day to control for the potential impact ofextreme trading does not change our results

We estimate equation (1) for each financial institution and each bond The coefficient θ ik represents the degree of informativeness of financial institution i, which initiated the trade on bond k A positive θ ik means that the trade initiator, financial institution i, consistently bought (sold) from (to) other financial institutions before an increase (decrease) in the price of bond k A significant value for θ ik implies that financial institution i is more informed than its trading counterparts with respect to bond k.

The larger the coefficient’s value, the greater the informational content of the trade

To examine how the relative informativeness of trades varies with type of financial institution, weestimate the following equation that relates trade informativeness to financial institutions characteristics:

zero otherwise The local small financial institution is our benchmark group Since all global financial

institutions are large in size, the size dummy SDUM takes a value of one if a financial institution is a global financial institution Therefore, the coefficient on GDUM measures the additional effect associated

with being a global financial institution beyond the size effect We estimate equation (2) using robust

clustered standard error regression In equation (2), we include all θs estimated in equation (1) Limiting observations to θs that are statically significantat p = 0.000 does not change our results We include bondfixed effect to control for possible unobserved heterogeneity among different bonds.

Table 3 presents the estimation results of equation (2) Panel A presents the results for the full

sample For the baseline 10-minute time interval, the estimated coefficient on the size effect SDUM

suggests that for every one million Turkish lira that large financial institutions trade, the bond pricechanges by TL 0.14 more than if the one million Turkish lira were traded by local small financialinstitutions Given the average bond price of TL 78 for the entire sample, the estimated bond price changesuggests the large financial institutions enjoy an 18 basis point price advantage Using the averageexchange rate during the sample period, 1.46 TL/USD, this effect corresponds to 10 U.S cents

The coefficient estimate on GDUM is also positive and statistically significant Specifically, for

every one million Turkish lira traded by global financial institutions, the price changes TL 0.09 or 6 U.S.cents more in a 10-minute interval than if the one million Turkish lira were traded by a typical largefinancial institution Global financial institutions thus enjoy an additional price advantage of 11 basispoints These results indicate that, on average, global financial institutions enjoy better pricing in bondtrading than local financial institutions, which is consistent with having an information advantage over

local financial institutions For the 30-minute interval, the coefficient estimate on GDUM is positive but

insignificant This suggests that the information advantage afforded global financial institutions is lived and consistent with the type of market where the information is mostly related to order flows on

short-global financial markets but not to fundamentals The coefficient estimate on SDUM, however, remains

positive and highly significant, indicating that large financial institutions benefit from their relative sizewhen the trading interval is extended to 30 minutes

To evaluate whether our findings are due to the dichotomy of foreign versus domestic financialinstitutions, we perform the regression analysis using only observations on Turkish financial institutions.Panel B reports the estimation results excluding foreign financial institutions The coefficient estimates on

SDUM and GDUM are very similar to those based on the entire sample The pricing benefit is slightly

higher at TL 0.12 (8 U.S cents) for global financial institutions than for large financial institutions Theinformation advantage afforded global financial institutions disappears after 30 minutes, as when usingthe full sample Further, the estimated size effect is also slightly larger than that in the full sample casewhen the pricing impact is measured 30 minutes after trade These results indicate that our findings onfavorable pricing in Turkish bond trading are more likely attributed to global financial institutions’ moreextensive network (relative to local financial institutions) than to the foreign versus domestic dichotomy

Panel C reports the estimation results using the subsample of liquid bonds We identify liquidbonds based on the Amihud (2002) liquidity measure, calculated for each bond as the absolute value ofdaily returns divided by daily dollar volume for the previous day, with low (below-median) Amihudmeasure bonds comprising the liquid bonds Panel D presents the results for the liquid bond subsample

excluding foreign financial institutions In both panels the coefficient estimates on SDUM and GDUM

remain quantitatively similar to the findings based on the full sample This suggests that more favorablepricing by global financial institutions is unlikely to be attributed to differences in bond liquidity

Finally, in unreported tables, we replicate our analysis by excluding financial institutions that aredesignated as primary dealers As explained above, primary dealers are required to quote a bid and askprice (though not necessarily the best bid and ask) in the limit order book of some bonds in the secondarymarket If these primary dealers systematically include non-global members, then it is possible that ourresults are driven by liquidity provision required from such members Our results are quantitativelysimilar when primary dealers are excluded from the sample This is in line with the Turkish market notexperiencing a buying/selling frenzy that requires the primary dealers to act as liquidity providers

4.3 Day-Trading Profitability

We next investigate how different financial institutions perform in terms of day-trading

profitability in the bond market To do so, we construct daily trading cycles for each bond k by financial institution i We assume that the initial the inventory is zero and that purchases increase inventory while

sales decrease it As the day unfolds the inventory level will typically hit zero several times The timebetween the adjacent zeros is considered a cycle and the profits associated with the transactions in eachcycle are calculated using the buy and sell prices and corresponding trade volumes

Using information on the percentage profit and funds invested in trading cycles for all bonds for a

given trader i on day t, we construct the day-trading profitability measure for each investor i on day t as

PRF is the weighted average percentage profit per trading cycle for investor i on day t We ignore direct

transaction costs in our calculation because they are very small at approximately 0.001% Moreover,since we are not privy to tick-by-tick TL/USD exchange rates, we calculate profits per cycle in Turkishlira Although lira and dollar profits may differ, there is no compelling reason for this difference to create

a short-term systematic bias

In Table 4, we report the PRF of local small, local large, and global financial institutions All

investor groups earn on average a negative profit on their day-trading activities Trades attributed toglobal financial institutions, however, have a smaller average loss (-0.02%) than the trades of local largefinancial institutions (-0.03%) or local small financial institutions (-0.04%) Overall, 33.3% of day trades

produce a positive profit The fraction of day trades with positive PRF is 37.2% for global financial

institutions, 34.4% for local large financial institutions, and 29.8% for local small financial institutions. 5

Although on average day-trading profitability is negative for all traders, financial institutions maystill make a net profit using other investment strategies, such as buy-and-hold, on certain bonds In our

institutional day traders, on average, profit The composition of trades in this market is dramatically different from the Bond and Bills Market, however For instance, in the Turkish market all of the traders are institutions, while institutions only account for 10.5% of the Taiwanese trade value

sample, the buy-and-hold strategy is used by both financial institutions that do not day trade (48% of thetraders do not participate in day-trading activities) and by day traders as part of their overall tradingstrategy Lending support to this view, day trades make up only 35% of trading volume (in U.S dollars).

In column 1 of Panel B, we report that global financial institutions earn significantly higher day-tradingprofits than local large financial institutions (p = 0.000), which in turn earn higher day-trading profits thanlocal small financial institutions (p = 0.000)

Barber et al (2004) suggest that frequent day traders perform better than infrequent day traders

In addition, if investors are overconfident in their trading skills, prior profitability will likely influencetheir participation decisions in day trading It is also plausible that traders learn from prior trading,adjusting their trading strategies based on prior day-trading successes and market conditions Because alarge proportion of financial institutions do not participate in day trading, we conduct joint estimation ofthe participation decision and day-trading profitability using Heckman’s (1979) self-selection model,where we specify the participation decision as a function of the trader’s prior day-trading successes andmarket conditions, as proxied by interest rate volatility

Specifically, we estimate the following model:

where PRF it is the percentage day-trading profit of trader i on day t, LPRF it is the percentage profit of

previous day-trading, PART it is the participation value, which takes value of one if financial institution i participates in day-trading on day t, and VOLINT t-1 is the standard deviation of the interest rate on theprevious day using 30-minute observations The interaction terms capture the impact of past tradingperformance and market conditions on the day-trading profitability of various financial institutions. 6

6 Our conclusions remain unaffected if we use alternative selection equation specifications such as including (1)constant, (2) squared lag profitability, and (2) more lags of prior volatility